Deployment scenarios. We have proposed several deployment scenarios to address various PLATOON project use cases. To distinguish between the Edge node and central computer, we have to point out that the Edge node only incorporates reduced PLATOON architecture. Minimally it should include data connector and Data analytics tools component. Therefore, it has to do at least some transformation of the input data or enable events triggering. On the other hand, due to reduced system resources of edge devices, it is not suitable to implement all the components at the edge. Furthermore, the main priority for the edge node is expected to be real-time processing and response. Data analytic tools at the edge usually process only raw data. The central layer in the use case scenarios presents the central computer, which can be deployed in the cloud, close to the premises of the pilot or even close to the asset from which it reads the data and to whom it provides the control signals. To conclude with, the main difference between the Edge node and a Central node is that the Edge node processes data from a single or limited number of assets locally (rapid response) but has reduced resources by means of processing power and deployed components of the PLATOON architecture. The central computer, on the contrary, processes data from multiple assets in a central environment (takes time to collect, process and send back data to each asset) but has much larger processing and storage resources which allows to do more complex operations and process more data at the same time obtaining larger generalisation capability.
Deployment scenarios. Exploiting different radio frequency (RF) propagation dynamics at different car- rier frequencies, CA is designed to boost data rates in overlapping cell areas [17]. Generally, higher carrier frequencies used for data transmission will have less cov- erage as they attenuate more. The following are the five identified cases under which CA can be implemented with the assumption of having two bandwidth segments with carrier frequencies at F1 and F2 where F2 >F1 [25].
Deployment scenarios. The typical small cell deployment scenarios are outdoor deployments urban areas, outdoor deployment in rural and mostly indoor deployments enterprise spaces (see Figure 8). In all scenarios the small cell deployments may be used to enhance coverage, in places where existing macrocells have coverage gaps (holes or blackspots), or macro coverage does not exist all. Furthermore, the small cells may provide capacity enhancements (by offloading traffic from macrocells) in densely occupied locations, which generate large traffic volume densities. In addition to providing enhanced connectivity, small cells provide improved user experience through presence- and location-based enabled by the limited footprint of small cells [SCF2013b], especially in indoor locations where satellite-positioning methods are not available. Moreover, recent developments towards 5G on mobile edge computing (MEC) or fog computing are leveraging small cells as computing platforms [Atreyam2016]. This enables distribution of cloud services to the edge of the network (closer to the user), thus reaching the user with maximum efficiency and reduced latency. Figure 8 Deployment scenarios [Collier2017]
